The macroeconomic impact of a dengue outbreak: Case studies from Thailand and Brazil.

BACKGROUND: Dengue is spreading in (sub)tropical areas, and half of the global population is at risk. The macroeconomic impact of dengue extends beyond healthcare costs. This study evaluated the impact of dengue on gross domestic product (GDP) based on approaches tailored to two dengue-endemic countries, Thailand and Brazil, from the tourism and workforce perspectives, respectively. FINDINGS: Because the tourism industry is a critical economic sector for Thailand, lost tourism revenues were estimated to analyze the impact of a dengue outbreak. An input-output model estimated that the direct effects (on international tourism) and indirect effects (on suppliers) of dengue on tourism reduced overall GDP by 1.43 billion US dollars (USD) (0.26%) in the outbreak year 2019. The induced effect (reduced employee income/spending) reduced Thailand's GDP by 375 million USD (0.07%). Overall, lost tourism revenues reduced Thailand's GDP by an estimated 1.81 billion USD (0.33%) in 2019 (3% of annual tourism revenue). An inoperability input-output model was used to analyze the effect of workforce absenteeism on GDP due to a dengue outbreak in Brazil. This model calculates the number of lost workdays associated with ambulatory and hospitalized dengue. Input was collected from state-level epidemiological and economic data for 2019. An estimated 22.4 million workdays were lost in the employed population; 39% associated with the informal sector. Lost workdays due to dengue reduced Brazil's GDP by 876 million USD (0.05%). CONCLUSIONS: The economic costs of dengue outbreaks far surpass the direct medical costs. Dengue reduces overall GDP and inflicts national economic losses. With a high proportion of the population lacking formal employment in both countries and low income being a barrier to seeking care, dengue also poses an equity challenge. A combination of public health measures, like vector control and vaccination, against dengue is recommended to mitigate the broader economic impact of dengue.

Life Years Gained From the FDA Accelerated Approval Program in Oncology: A Portfolio Model.

BACKGROUND: Although the FDA Accelerated Approval Program (AAP) has come under scrutiny, the population-level health benefit of the program has not been quantified. Therefore, the objective of this study was to estimate the number of life years gained among patients with cancer that can be attributable to the therapies receiving FDA accelerated approvals in oncology between 2006 and 2022 in the United States. METHODS: The data sources used were FDA listings, FDA approval letters and labels, published clinical trial data and other publications including relative effectiveness estimates, and the Ipsos Oncology Uptake Tool for product uptake. Data for 130 oncology treatments approved by the FDA under the AAP were extracted and validated. We developed a decision analytic model to estimate the survival gain for each indication and to accumulate life years gained for consecutive cohorts of patients receiving the therapies. Life year gains were estimated with and without the AAP, and the incremental life years gained were attributed to the program. RESULTS: The analysis estimated that through December 2022 in the United States, the program gained approximately 263,000 life years across 69 products for which overall survival data were available, for approximately 911,000 patients with cancer. CONCLUSIONS: Policy discussions about the evaluation of AAP cannot be complete without assessing its impact on its most important target outcome: patient survival. To date, there has been no estimation of the life year gain delivered by the AAP. Our research shows that substantial number of life years were gained for patients with high unmet need by the cancer therapies approved through the program.

Public health impact and economic value of booster vaccination with Pfizer-BioNTech COVID-19 vaccine, bivalent (Original and Omicron BA.4/BA.5) in the United States.

OBJECTIVE: To assess the public health impact and economic value of booster vaccination with the Pfizer-BioNTech COVID-19 Vaccine, Bivalent in the United States. METHODS: A combined cohort Markov decision tree model estimated the cost-effectiveness and budget impact of booster vaccination compared to no booster vaccination in individuals aged ≥5 years. Analyses prospectively assessed three scenarios (base case, low, high) defined based upon the emergence (or not) of subvariants, using list prices. Age-stratified parameters were informed by literature. The cost-effectiveness analysis estimated cases, hospitalizations and deaths averted, Life Years (LYs) and Quality Adjusted Life Years (QALYs) gained, the incremental cost-effectiveness ratio (ICER), the net monetary benefit (NMB), and the Return on Investment (ROI). The budget impact analyses used the perspective of a hypothetical 1-million-member plan. Sensitivity analyses explored parameter uncertainty. Conservatively, indirect effects and broad societal benefits were not considered. RESULTS: The base case predicted that, compared to no booster vaccination, the Pfizer-BioNTech COVID-19 Vaccine, Bivalent could result in ∼3.7 million fewer symptomatic cases, 162 thousand fewer hospitalizations, 45 thousand fewer deaths, 373 thousand fewer discounted QALYs lost, and was cost-saving. Using a conservative value of $50,000 for 1 LY, every $1 invested yielded estimated $4.67 benefits. Unit costs, health outcomes and effectiveness had the greatest impact on results. At $50,000 per QALY gained, the booster generated a 34.2 billion NMB and probabilistic sensitivity analyses indicated a 92% chance of being cost-saving and 98% of being cost-effective. The bivalent was cost-saving or highly cost-effective in high and low scenarios. In a hypothetical 1-million-member health plan population, the vaccine was predicted to be a budget-efficient solution for payers. CONCLUSIONS: Booster vaccination with the Pfizer-BioNTech COVID-19 Vaccine, Bivalent for the US population aged ≥5 years could generate notable public health impact and be cost-saving based on the findings of our base case analyses.

The Societal Value of Vaccines: Expert-Based Conceptual Framework and Methods Using COVID-19 Vaccines as a Case Study.

Health technology assessments (HTAs) of vaccines typically focus on the direct health benefits to individuals and healthcare systems. COVID-19 highlighted the widespread societal impact of infectious diseases and the value of vaccines in averting adverse clinical consequences and in maintaining or resuming social and economic activities. Using COVID-19 as a case study, this research work aimed to set forth a conceptual framework capturing the broader value elements of vaccines and to identify appropriate methods to quantify value elements not routinely considered in HTAs. A two-step approach was adopted, combining a targeted literature review and three rounds of expert elicitation based on a modified Delphi method, leading to a conceptual framework of 30 value elements related to broader health effects, societal and economic impact, public finances, and uncertainty value. When applying the framework to COVID-19 vaccines in post-pandemic settings, 13 value elements were consensually rated highly important by the experts for consideration in HTAs. The experts reviewed over 10 methods that could be leveraged to quantify broader value elements and provided technical forward-looking recommendations. Limitations of the framework and the identified methods were discussed. This study supplements ongoing efforts aimed towards a broader recognition of the full societal value of vaccines.

A Framework for Integrating Continuous Glucose Monitor-Derived Metrics into Economic Evaluations in Type 1 Diabetes.

Economic models in type 1 diabetes have relied on a change in haemoglobin A1c as the link between the blood glucose trajectory and long-term clinical outcomes, including microvascular and macrovascular disease. The landscape has changed in the past decade with the availability of regulatory approved, accurate and convenient continuous glucose monitoring devices and their ability to track patients' glucose levels over time. The data emerging from continuous glucose monitoring have enriched the clinical understanding of the disease and indirectly of patients' behaviour. This has triggered the development of new measures proposed to better define the quality of glycaemic control, beyond haemoglobin A1c. The objective of this paper is to review recent developments in clinical knowledge brought into focus with the application of continuous glucose monitoring devices, and to discuss potential approaches to incorporate the concepts into economic models in type 1 diabetes. Based on a targeted review and a series of multidisciplinary workshops, an influence diagram was developed that captures newer concepts (e.g. continuous glucose monitoring metrics) that can be integrated into economic models and illustrates their association with more established concepts. How the additional continuous glucose monitoring-based indicators of glycaemic control may contribute to economic modelling beyond haemoglobin A1c, and more accurately reflect the economic value of novel type 1 diabetes treatments, is discussed.

Public health impact of the Pfizer-BioNTech COVID-19 vaccine (BNT162b2) in the first year of rollout in the United States.

BACKGROUND: As the body of evidence on COVID-19 and post-vaccination outcomes continues to expand, this analysis sought to evaluate the public health impact of the Pfizer-BioNTech COVID-19 Vaccine, BNT162b2, during the first year of its rollout in the US. METHODS: A combined Markov decision tree model compared clinical and economic outcomes of the Pfizer-BioNTech COVID-19 Vaccine (BNT162b2) versus no vaccination in individuals aged ≥12 years. Age-stratified epidemiological, clinical, economic, and humanistic parameters were derived from existing data and published literature. Scenario analysis explored the impact of using lower and upper bounds of parameters on the results. The health benefits were estimated as the number of COVID-19 symptomatic cases, hospitalizations and deaths averted, and Quality Adjusted Life Years (QALYs) saved. The economic benefits were estimated as the amount of healthcare and societal cost savings associated with the vaccine-preventable health outcomes. RESULTS: It was estimated that, in 2021, the Pfizer-BioNTech COVID-19 Vaccine (BNT162b2) contributed to averting almost 9 million symptomatic cases, close to 700,000 hospitalizations, and over 110,000 deaths, resulting in an estimated $30.4 billion direct healthcare cost savings, $43.7 billion indirect cost savings related to productivity loss, as well as discounted gains of 1.1 million QALYs. Scenario analyses showed that these results were robust; the use of alternative plausible ranges of parameters did not change the interpretation of the findings. CONCLUSIONS: The Pfizer-BioNTech COVID-19 Vaccine (BNT162b2) contributed to generate substantial public health impact and vaccine-preventable cost savings in the first year of its rollout in the US. The vaccine was estimated to prevent millions of COVID-19 symptomatic cases and thousands of hospitalizations and deaths, and these averted outcomes translated into cost-savings in the billions of US dollars and thousands of QALYs saved. As only direct impacts of vaccination were considered, these estimates may be conservative.

Modeling Challenges in Cost-Effectiveness Analysis of First-Line Immuno-Oncology Therapies in Non-small Cell Lung Cancer: A Systematic Literature Review.

INTRODUCTION: The introduction of immuno-oncology (IO) therapies has changed the treatment landscape of non-small cell lung cancer (NSCLC). Numerous cost-effectiveness analyses (CEAs) and technology appraisals (TAs) evaluating IO therapies have been recently published. OBJECTIVE: We reviewed economic models of first-line (1L) IO therapies for previously untreated advanced or metastatic NSCLC to identify methodological challenges associated with modeling cost effectiveness from published literature and TAs and to make recommendations for future CEAs in this disease area. METHODS: A systematic literature review was conducted following Cochrane and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched MEDLINE, Embase, EconLit (January 2009-January 2020), and select conferences (since 2016) for CEAs of 1L IO treatments in patients with recurrent or metastatic, epidermal growth factor receptor (EGFR)/anaplastic lymphoma kinase (ALK) mutation-negative NSCLC, published in English. TAs from England, Scotland, Canada, Australia, Germany, and France were also examined. Two reviewers screened the results and extracted the data. The quality of the CEAs was described using the Drummond checklist. RESULTS: In total, 46 records reporting on 38 unique models met protocol-defined criteria and were included. Five models adjusted for treatment switching or crossover in base-case analyses, and the remainder considered treatment switching or crossover to represent clinical practice and made no adjustment. Seven models used external real-world data for survival modeling or extrapolation validation. Six models that assumed long-term treatment benefit stopped at 3 or 5 years after initiation. Seven models used the observed time-on-treatment distribution from the trial, and eight used progression-free survival for treatment duration. All models compared one or more IO monotherapies or combination therapies with chemotherapy. Only one study directly compared different IO agents but did not consider the concordance issue across programmed death-ligand 1 (PD-L1) testing methods. Utilities were modeled by health state in 12 models, four applied a time-to-death approach, and ten explored both. None applied cure models. CONCLUSION: Variations in methodological challenges were seen across studies. Previous models took approaches that were followed in subsequent models, such as a 2-year stopping rule of IO duration or treatment-effect waning. Challenges such as heterogeneity in PD-L1 testing and survival extrapolation and validation using real-world data should be further considered for future models in advanced or metastatic NSCLC.